Interdisciplinarv research focused on the development and evaluation of technology-based behavior change interventions for substance use disorders should be disseminated to a broad array of stakeholders across different organization levels to increase its likelihood of having a marked public health impact. While it is widely acknowledged across health service systems that treatment practices often do not align with research knowledge, substance abuse treatment programs have been particularly slow to adopt evidence-based practices (1,2). While substance abuse treatment providers are generally open to learning new treatment practices, particularly those they believe can help them do their job, there is considerable variability in practitioner readiness to adopt evidence-based interventions (EBls)(6). Practitioners grounded in more traditional addiction models, such as AA or a related 12-step, are less likely to use EBIs than practitioners trained in evidence-based practices(2). Clinicians in settings involved in research (e.g..Clinical Trials Network) are among those most likely to adopt evidence-based treatment practices ( 7). Many person-delivered EBIs, however, are labor-intensive and costly to deliver, and therefore do not align with the time and budget realities of most substance abuse treatment settings. Similar barriers to EBI adoption exist in other systems where individuals with substance use disorders could receive care, such as criminal justice and health care settings (4). In addition to practitioners' philosophies regarding treatment and organizational barriers to adoption of EBIs, the channels of knowledge transfer about treatment innovations are different for researchers vs. service providers. Researchers typically rely on academic journals for the dissemination of new knowledge, while providers are more likely to learn about new practices through colleagues or clients, seeing something in the news, reading a brochure, via a SAMHSA/CSAT-supported Treatment Improvement Protocol (TIP) or attending a professional conference(2). To achieve the goal of building evidence-based program knowledge among practitioners, program directors and policy makers, research information about treatment innovations needs to be synthesized and translated in a way that is accessible to the intended audience and clearly describes implications of the work for practice, organization, and policy (2,8). Furthermore, dissemination activities should be accompanied by evaluations to identify dissemination strategies that are most effective. Momentum for promoting evidence-based substance abuse treatment has grown in the past decade. Many federal funding agencies, such as SAMHSA/CSAT and CDC, require use of EBIs for organizations to receive funding(8). The efforts of NIDA and SAMHSA/CSAT to bridge the gap between research and practice have yielded important initiatives, including the Clinical Trials Network (CTN: NIDA), Addiction Technology Transfer Centers (CSAT/NIDA), Treatment Improvement Protocols (TIP: SAMHSA/CSAT, and the National Registry for Evidence-Based Programs and Practices (NREPP: SAMHSA). Still, the data highlight the continued challenges of EBI dissemination. To effectively translate the science of technology-based interventions for individuals with substance use disorders to practice, dissemination efforts must reach the diverse stakeholder and service system audiences, including patients/consumers, practitioners, program directors and administrators, researchers, service payors and policy-makers, with meaningful information and support material to optimize potential for program adoption within service systems. This is the goal of the Dissemination and Implementation (D&l) Core of the Center for Technology and Health (CTH). The Diffusion of Innovation(5) (DOI) theory is a useful framework to guide dissemination plans for technoloqv-based behavioral interventions to optimize their likelihood of adoption.The DOI model describes the process through which an innovation, defined as an idea perceived as new (i.e., technology-based treatments for substance abuse), spreads over time within a system (5). The model derives from a rich, multidisciplinary evidence base on the uptake of innovations, ranging from hybrid seed corn to HIV prevention (9,10). This groundbreaking model has contributed to a greater understanding of behavioral and organizational change, and has a broad scope of practical applications in the field of public health (10). The DOI model distinguishes between diffusion (passive spread of innovation), dissemination (active and planned efforts to persuade target groups to adopt an innovation), implementation (active and planned efforts to mainstream an innovation within an organization), and sustainability (making an innovation routine)(9). The (passive) diffusion of innovations typically occurs via unplanned, informal, decentralized, and horizontal communication channels (e.g., peer-to-peer), while active dissemination of an innovation is typically more centralized and likely to occur through vertical communication mechanisms (e.g., provider-program director)(9). The DOI model frames adoption of innovations as a function of characteristics of the innovation (e.g., technology-based behavior change interventions for individuals with substance use disorders), of individual adopters (e.g., consumers, substance abuse treatment counselors, physicians, drug court personnel) and of the organizations or systems within which the innovation could be adopted (e.g., criminal justice, substance abuse treatment centers, HIV clinics) (5). Characteristics of innovations that increase the likelihood of adoption of an innovation, such as a technology-based behavioral intervention, include (5,9) : (a) Relative advantage: extent to which potential end-users perceive a clear advantage for using the innovation, such as better treatment effectiveness, cost-effectiveness, or improved job performance, (b) Compatibility: extent to which innovations are compatible with intended users' values, norms, and perceived needs. Interventions to improve feasibility and workability of innovations at both individual and organizational levels can improve chances of successful adoption (c) Simplicity: extent to which innovations are perceived as easy to use and understand. Training and education can help build competence regarding use, (d) Trialability: extent to which intended users can try out or experiment with the innovation before making a decision about adoption, (e) Flexibility: degree to which an innovation can be readily integrated into a system's infrastructure, (f) Knowledge: extent of ready knowledge transfer about how to use an innovation, and (g) Support: extent to which innovations include augmented support (e.g., on-demand support, customization, help desk, etc., as needed)